Identifying a task execution resource of a dispersed storage network

ABSTRACT

A computing device includes memory, an interface, and a processing module. The processing module is operable to, for a plurality of dispersed storage network (DSN) units of a DSN, determine to perform a DSN level task for a range of DSN addresses. The processing module is also operable to execute a scoring function for the plurality of DSN units using one or more properties of the range of DSN addresses and one or more properties of each of the plurality of DSN units to produce a scoring resultant. The processing module is also operable to identify a DSN unit of the plurality of DSN units to execute the DSN level task based on the scoring resultant. The processing module is operable to instruct the identified DSN unit to execute the DSN level task for the range of DSN addresses.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §120 as a continuation of U.S. Utility application Ser. No.14/721,723, entitled “IDENTIFYING A TASK EXECUTION RESOURCE OF ADISPERSED STORAGE NETWORK”, filed May 26, 2015, which claims prioritypursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No.62/031,320, entitled “REBUILDING DATA IN A DISPERSED STORAGE NETWORK”,filed Jul. 31, 2014, both of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. Utilitypatent application for all purposes.

U.S. Utility patent application Ser. No. 14/721,723 also claims prioritypursuant to 35 U.S.C. §120 as a continuation-in-part of U.S. Utilityapplication Ser. No. 14/707,943, entitled “ACCESSING A DISPERSED STORAGENETWORK”, filed May 8, 2015, which claims priority pursuant to 35 U.S.C.§119(e) to U.S. Provisional Application No. 62/019,074, entitled“UTILIZING A DECENTRALIZED AGREEMENT PROTOCOL IN A DISPERSED STORAGENETWORK”, filed Jun. 30, 2014, both of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc., on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of selecting theresource in accordance with the present invention;

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIGS. 41A-F are a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 41G is a flowchart illustrating an example of recovering an encodeddata slice in accordance with the present invention;

FIGS. 42A and 42C are a schematic block diagram of another embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention;

FIGS. 42B and 42D are a schematic block diagram of another embodiment ofa distributed storage and task (DST) integrity processing unit inaccordance with the present invention;

FIG. 42E is a flowchart illustrating an example of identifying a taskexecution resource in accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 43B is a flowchart illustrating an example of selecting a memorydevice in accordance with the present invention;

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44B is a flowchart illustrating another example of selecting thememory device in accordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 45B is a flowchart illustrating an example of migrating encodeddata slices in accordance with the present invention;

FIG. 46A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 46B is a flowchart illustrating an example of temporarily storingrebuilt encoded data slices in accordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 47B is a flowchart illustrating an example of testing a storageunit in accordance with the present invention;

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 48B is a flowchart illustrating an example of utilizing a vaultstructure in a dispersed storage network (DSN) in accordance with thepresent invention;

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 49B is a flowchart illustrating an example of selecting storageresources in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general, and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first—third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first—third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module 350 that includes a set of deterministicfunctions 1-N, a set of normalizing functions 1-N, a set of scoringfunctions 1-N, and a ranking function 352. Each of the deterministicfunction, the normalizing function, the scoring function, and theranking function 352, may be implemented utilizing the processing module84 of FIG. 3. The decentralized agreement module 350 may be implementedutilizing any module and/or unit of a dispersed storage network (DSN).For example, the decentralized agreement module is implemented utilizingthe distributed storage and task (DST) client module 34 of FIG. 1.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSTclient module 34 of FIG. 1, a DST processing unit 16 of FIG. 1, a DSTintegrity processing unit 20 of FIG. 1, a DSTN managing unit 18 of FIG.1, a user device 12 of FIG. 1, and a user device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 (e.g., a DST EX unit pool 2 of FIG.40C) to a source name as the asset ID to produce a combined value andperforms the mask generating function on the combined value to produceinterim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 40B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and thedistributed storage and task network (DSTN) module 22 of FIG. 1.Hereafter, the DSTN module 22 may be interchangeably referred to as aDSN memory. The DST processing unit 16 includes a decentralizedagreement module 380 and the DST client module 34 of FIG. 1. Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. The DSTN module 22includes a plurality of DST execution (EX) unit pools 1-P. Each DSTexecution unit pool includes a one or more sites 1-S. Each site includesone or more DST execution units 1-N. Each DST execution unit may beassociated with at least one pillar of N pillars associated with aninformation dispersal algorithm (IDA), where a data segment is dispersedstorage error encoded using the IDA to produce one or more sets ofencoded data slices, and where each set includes N encoded data slicesand like encoded data slices (e.g., slice 3's) of two or more sets ofencoded data slices are included in a common pillar (e.g., pillar 3).Each site may not include every pillar and a given pillar may beimplemented at more than one site. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, a DSTexecution unit may be referred to interchangeably as a storage unit anda set of DST execution units may be interchangeably referred to as a setof storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selecting of theresources includes utilizing a decentralized agreement function of thedecentralized agreement module 380, where a plurality of locations areranked against each other. The selecting may include selecting onestorage pool of the plurality of storage pools, selecting DST executionunits at various sites of the plurality of sites, selecting a memory ofthe plurality of memories for each DST execution unit, and selectingcombinations of memories, DST execution units, sites, pillars, andstorage pools.

In an example of operation, the DST client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theDST client module 34 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, and a plurality ofsets of slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSTN module 22. Thedetermining may be based on one or more of the data name, the requestingentity ID, a predetermination, a lookup, a DSN performance indicator,and interpreting an error message. For example, the DST client module 34selects the DST execution unit pool as a first resource level and a setof memory devices of a plurality of memory devices as a second resourcelevel based on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality resource levels, the DST client module 34,for each resource level, issues a ranked scoring information request 384to the decentralized agreement module 380 utilizing the DSN address asan asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DST client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DST client module 34 identifies one or moreresources associated with the resource level based on the ranked scoringinformation 386. For example, the DST client module 34 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34accesses the DSTN module 22 based on the identified one or moreresources associated with each resource level. For example, the DSTclient module 34 issues resource access requests 388 (e.g., write slicerequests when storing data, read slice requests when recovering data) tothe identified DST execution unit pool, where the resource accessrequests 388 further identify the identified set of memory devices.Having accessed the DSTN module 22, the DST client module 34 receivesresource access responses 390 (e.g., write slice responses, read sliceresponses). The DST client module 34 issues the data access response 392based on the received resource access responses 390. For example, theDST client module 34 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 394where a processing module (e.g., of a distributed storage and task (DST)client module) receives a data access request from a requesting entity.The data access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 396 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality resource levels associated with the DSN memory. The selectingmay be based on one or more of a predetermination, a range of weightsassociated with available resources, a resource performance level, and aresource performance requirement level. For each resource level, themethod continues at step 400 where the processing module determinesranked scoring information. For example, the processing module issues aranked scoring information request to a decentralized agreement modulebased on the DSN address and receives corresponding ranked scoringinformation for the resource level, where the decentralized agreementmodule performs a decentralized agreement protocol function on the DSNaddress using the associated resource identifiers and resource weightsfor the resource level to produce the ranked scoring information for theresource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIGS. 41A-F are a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) integrity processing unit 20 of FIG. 1, the network 24 ofFIG. 1, and a plurality of storage unit sets 1-3. The DST integrityprocessing unit 20 includes a decentralized agreement module 410 and theDST client module 34 of FIG. 1. The decentralized agreement module 410may be implemented utilizing the decentralized agreement module 350 ofFIG. 40A. Each storage unit set includes a set of DST execution units1-n. Each DST execution unit may be implemented utilizing the DSTexecution unit 36 of FIG. 1. Hereafter, each DST execution unit may beinterchangeably referred to as a storage unit, a storage unit set may beinterchangeably referred to as a set of storage units and/or a storagepool, and the plurality of storage unit sets may be interchangeablyreferred to as a DSN memory and/or a plurality of storage pools

The DSN functions to recover an encoded data slice, where a data objectis divided into a plurality of N data segments, where each data segmentis dispersed storage error encoded to produce a set of encoded dataslices 1-n, and where each set of encoded data slices is stored in atleast one set of DST execution units. For example, the DST client module34 dispersed storage error encodes the data object to produce a firstset of encoded data slices 1-1 through n−1, a second set of encoded dataslices 1-2 through n−2, a third set of encoded data slices 1-3 throughn−3, etc., through an Nth set of encoded data slices 1-N through n−N,selects the storage unit set 1 for storage of the plurality of sets ofencoded data slices in accordance with a scoring function (e.g., byidentifying a storage unit set 1 as a highest ranked storage unit set ofthe plurality of storage unit sets for the data object utilizing thedecentralized agreement module 410), and facilitates storage of theplurality of sets of encoded data slices in the DST execution units 1-nof the storage unit set 1.

FIG. 41A illustrates steps of an example of operation of the recoveringof the encoded data slice where the DST integrity processing unit 20receives a DSN retrieval request regarding the data object. The DSNretrieval request includes one of a variety of DSN retrieval requestcomponents. A first DSN retrieval request component includes a readrequest for reading the data object from the DSN memory, where afavorable response to a corresponding one of a set of retrieval requestsincludes sending a corresponding portion of the data object. A secondDSN retrieval request component includes a list request for storageinformation regarding the data object, where the favorable response to acorresponding one of the set of retrieval requests includes sendingstorage information (e.g., a slice name, a DSN address, a revisionlevel, etc.) regarding the corresponding portion of the data object. Athird DSN retrieval request component includes an individual readrequest regarding the corresponding portion of the data object, wherethe favorable response includes sending the corresponding portion of thedata object and wherein the set of primary storage units only includesthe primary storage unit. A fourth DSN retrieval request componentincludes an individual list request regarding the corresponding portionof the data object, where the favorable response includes sending thestorage information of the corresponding portion of the data object andwherein the set of primary storage units only includes the primarystorage unit.

Having received the DSN retrieval request, the DST integrity processingunit 20 performs a scoring function using one or more properties of theDSN retrieval request (e.g., a DSN address, a source name, a slice name)and one or more properties of the DSN memory of the DSN (e.g.,identities of the storage pools, identities of the storage units,weighting factors of the storage pools, weighting factors of the storageunits) to produce a storage scoring resultant, where the DSN memoryincludes a plurality of storage units that are logically arranged intothe plurality of storage pools.

The performing the scoring function includes a variety of approaches. Ina first scoring function approach, the DST client module 34 selects aresource level and selects the one or more properties of the DSN memoryfrom a plurality of properties of the DSN memory based on the selectedresource level. The DST client module 34 issues a ranked scoringinformation request 412 to the decentralized agreement module 410, wherethe ranked scoring information request 412 includes one or more of theselected resource level, the one or more properties of the DSN memory,and the one or more properties of the DSN retrieval request. Thedecentralized agreement module 410 calculates, based on the selectedresource level, a plurality of storage values (e.g., a score associatedwith each storage pool) based on the one or more properties of the DSNretrieval request and the one or more properties of DSN memory. Havingcalculated the plurality of storage values, the decentralized agreementmodule 410 performs a ranking function on the plurality of storagevalues to produce the storage scoring resultant and sends ranked scoringinformation 414, that includes the storage scoring resultant, to the DSTclient module 34.

In a second scoring function approach, the performing the scoringfunction includes the DST client module 34 selecting a storage poollevel indication as the resource level and selecting a storage poolidentifier and a storage pool weighting factor for each of the pluralityof storage pools to produce a plurality of storage pool identifiers anda plurality of storage pool weighting factors, where the one or moreproperties of DSN memory includes the plurality of storage poolidentifiers and the plurality of storage pool weighting factors. The DSTclient module 34 selects the source name of the DSN retrieval request asthe one or more properties of the DSN retrieval request. Thedecentralized agreement module 410 performs a series of functions on thesource name based on the plurality of storage pool identifiers and theplurality of storage pool weighting factors to produce the plurality ofstorage values and performs a ranking function on the plurality ofstorage values to produce the storage scoring resultant. For example, aseries of the series of functions includes a deterministic function ofthe source name and one of the storage pool identifiers to produce aninterim result, a normalizing function of the interim result to producea normalized interim result, and a scoring function of the normalizedinterim result and a corresponding one of the storage pool weightingfactors to produce a storage value of the plurality of storage values(e.g., the ranked scoring information 414).

In a third scoring function approach, the performing the scoringfunction includes the DST client module 34 selecting a storage unitlevel indication as the resource level and selecting a storagesite-storage unit identifier and a storage site-storage weighting factorfor each of the plurality of storage units to produce a plurality ofstorage site-storage unit identifiers and a plurality of storagesite-storage unit weighting factors, where the one or more properties ofDSN memory includes the plurality of storage site-storage unitidentifiers and the plurality of storage site-storage unit weightingfactors. The DST client module 34 selects the source name of the DSNretrieval request as the one or more properties of the DSN retrievalrequest. The decentralized agreement module 410 performs the series offunctions on the source name based on the plurality of storagesite-storage unit identifiers and the plurality of storage site-storageunit weighting factors to produce the plurality of storage values andperforms the ranking function on the plurality of storage value toproduce the storage scoring resultant (e.g., the ranked scoringinformation 414).

Having performed the scoring function to produce the storage scoringresultant, the DST client module 34 identifies a set of primary storageunits of the plurality of storage units based on the storage scoringresultant. For example, the DST client module 34 identifies the storageunits of the storage unit set 1 as the set of primary storage units whenthe storage scoring resultant indicates that the storage unit set 1 isassociated with a highest of the ranked scores of the storage scoringresultant.

Having identified the set of primary storage units, the DST clientmodule 34 sends, via the network 24, a set of retrieval requests to theset of primary storage units regarding the DSN retrieval request. Forexample, the DST client module 34, issues list slice requests to the DSTexecution units of the storage unit set 1 and receives a sliceavailability information 1 from the storage unit set 1 in response,where the slice availability information 1 indicates whether a storageerror (e.g., a missing encoded data slice, a corrupted encoded dataslice) has occurred with regards to one or more encoded data slices ofat least some of the plurality of sets of encoded data slices of thedata object.

When a primary storage unit of the set of primary storage units does notprovide a favorable response to a corresponding one of the set ofretrieval requests regarding a corresponding portion of the data object,the DST client module 34 indicates the storage error for thecorresponding portion of the data object. For example, the DST clientmodule 34 interprets the slice availability information 1 from the DSTexecution units of the storage unit set 1 to determine that the storageerror has occurred with regards to the encoded data slice 2-3 from theprimary storage unit (e.g., DST execution unit 2 of the storage unit set1).

FIG. 41B illustrates further steps of the example of operation of therecovering of the encoded data slice where, the DST client module 34,when the primary storage unit of the set of primary storage units doesnot provide the favorable response, uses the storage scoring resultantto identify an alternative storage unit of the plurality of storageunits regarding the corresponding portion of the data object. Forexample, the DST client module 34 selects the DST execution unit 2 ofthe storage unit set 3 as the alternative storage unit when the sliceavailability information 1 indicates the unfavorable response (e.g., amissing encoded data slice 2-3 from primary storage unit DST executionunit 2 of the storage unit set 1), the storage scoring resultant (e.g.,ranked scoring information 414) indicates that the storage unit set 3 isassociated with a next highest score, and slice availability informationfrom the DST execution unit 2 of the storage unit set 3 indicates thatthe encoded data slice 2-3 is available (e.g., verifying encoded dataslice availability as part of the identifying of the alternative storageunit).

When the alternative storage unit is selected, the DST client module 34sends, via the network 24, the corresponding one of the set of retrievalrequests to the alternative storage unit. For example, the DST clientmodule 34 sends a slice availability information request to the DSTexecution unit 2 of the storage unit set 3, where the slice availabilityinformation request is with regards to the encoded data slice 2-3. Thealternative storage unit issues, via the network 24, a correspondingslice availability information response to the DST client module 34 withregards to the corresponding one of the set of retrieval requests.Alternatively, or in addition to, the DST client module 34 sends thecorresponding one of the set of retrieval requests to more than onealternative storage unit in accordance with the storage scoringresultant (e.g., next highest scores). For example, the DST clientmodule 34 receives, via the network 24, slice availability information2-3 from two or more alternative storage units of the storage unit sets3 and 2 indicating whether the encoded data slice 2-3 is available andindicates that the DST execution unit 2 of the storage unit set 3 isverified as the identified alternative storage unit.

FIG. 41C illustrates further steps of the example of operation of therecovering of the encoded data slice where the DST client module 34,when the alternative storage unit is identified (e.g., DST executionunit 2 of the storage unit set 3 holds encoded data slice 2-3 asavailable), facilitates transfer of the corresponding portion of thedata object from the alternative storage unit to the primary storageunit when the primary storage unit is available. For example, the DSTclient module 34 issues, via the network 24, a transfer request forencoded data slice 2-3 to the DST execution unit 2 of the storage unitset 3, where the DST execution unit 2 of the storage unit set 3 sends,via the network 24, the encoded data slice 2-3 to the DST execution unit2 of the storage unit set 1 for storage.

FIG. 41D illustrates steps of another example of operation of therecovering of the encoded data slice where the DST integrity processingunit 20, having received the DSN retrieval request regarding the dataobject, performs the scoring function using the one or more propertiesof the DSN retrieval request and the one or more properties of the DSNmemory to produce the storage scoring resultant. Having produced thestorage scoring resultant, the DST client module 34 identifies the setof primary storage units based on the storage scoring resultant. Forexample, the DST client module 34 identifies the DST execution units ofthe storage unit set 1 as the primary storage units when the storagepool level has been selected as the resource level.

Having identified the primary storage units, the DST client module 34sends, via the network 24, the set of retrieval requests to the set ofprimary storage units regarding the DSN retrieval request. For example,the DST client module 34 issues slice availability information requests(e.g., list slice request) for substantially all of the plurality ofsets of encoded data slices to the set of DST execution units of thestorage unit set 1. Having sent the retrieval requests, the DST clientmodule 34 receives slice availability information from at least some ofthe storage units of the set of primary storage units. For example, theDST client module 34 receives slice availability information 1 from DSTexecution units 1, 3-n.

Having received the slice availability information, the DST clientmodule 34 indicates whether a primary storage unit of the set of primarystorage units provides a favorable response to a corresponding one ofthe set of retrieval requests regarding a corresponding portion of thedata object. For example, the DST client module 34 indicates that theprimary storage unit (e.g., DST execution unit 2) did not provide thefavorable response when encoded data slices 2-1, 2-2, 2-3, through 2-Nare associated with a storage error (e.g., missing slices) when notreceiving any response from the DST execution unit 2 (e.g., DSTexecution unit 2 is unavailable).

FIG. 41E illustrates further steps of the other example of operation ofthe recovering of the encoded data slice. When the primary storage unitof the set of primary storage units does not provide the favorableresponse to a corresponding one of the set of retrieval requestsregarding the corresponding portion of the data object, the DST clientmodule 34 uses the storage scoring resultant to identify the alternativestorage unit of the plurality of storage units regarding thecorresponding portion of the data object. For example, the DST clientmodule 34 interprets the ranked scoring information 414 to select thestorage unit set 3 as a next highest ranked storage pool (e.g., rank 2)and the storage unit set 2 has a further next highest ranked (e.g., rank3) storage pool.

Having selected the alternative storage unit, the DST client module 34sends the corresponding one of the set of retrieval requests for thealternative storage unit. For example, the DST client module 34 sendsslice availability information requests to the storage unit sets 3-2,receives slice availability information 2-3 in response, and does notidentify the alternative storage unit, where the received sliceavailability information indicates that the encoded data slices 2-1,2-2, 2-3, through 2-N are not available from the storage unit sets 2-3.

FIG. 41F illustrates further steps of the other example of operation ofthe recovering of the encoded data slice where the DST client module 34,when the alternative storage unit is not identified, issues a rebuildingfunction for the corresponding portion of the data object, where arebuilt corresponding portion of the data object is to be stored in thealternative storage unit (e.g., DST execution unit 2 of the storage unitset 3). For example, the DST client module 34 issues rebuilding slicesrequests 416 (e.g., read slice requests) to at least a decode thresholdnumber of storage units of the set of primary storage units, receivesrebuilding slices 418 from at least some of the storage units,re-generates the corresponding portion of the data object (e.g.,rebuilds the encoded data slices 2-1, 2-2, 2-3, through 2-N) utilizingthe rebuilding slices 418, and sends the rebuilt slices 420 as thecorresponding portion of the data object to a highest ranked availablestorage unit (e.g., DST execution unit 2 of the storage unit set 3) forstorage.

FIG. 41G is a flowchart illustrating an example of recovering an encodeddata slice. In particular, a method is presented for use in conjunctionwith one or more functions and features described in conjunction withFIGS. 1-39, 41A-F, and also FIG. 41G. The method begins or continues atstep 430 where a processing module of a computing device of one or morecomputing devices of a dispersed storage network (DSN) receives a DSNretrieval request regarding a data object. The method continues at step432 where the processing module performs a scoring function using one ormore properties of the DSN retrieval request and one or more propertiesof DSN memory of the DSN to produce a storage scoring resultant, wherethe DSN memory includes a plurality of storage units that are logicallyarranged into a plurality of storage pools. The performing the scoringfunction includes a variety of approaches. In a first scoring functionapproach, the performing the scoring function includes the processingmodule selecting a resource level, selecting the one or more propertiesof the DSN memory from a plurality of properties of the DSN memory basedon the selected resource level, calculating, based on the selectedresource level, a plurality of storage values based on the one or moreproperties of the DSN retrieval request and the one or more propertiesof DSN memory, and performing a ranking function on the plurality ofstorage values to produce the storage scoring resultant.

In a second scoring function approach, the performing the scoringfunction includes the processing module selecting a storage pool levelindication as a resource level, selecting a storage pool identifier anda storage pool weighting factor for each of the plurality of storagepools to produce a plurality of storage pool identifiers and a pluralityof storage pool weighting factors, where the one or more properties ofDSN memory includes the plurality of storage pool identifiers and theplurality of storage pool weighting factors, selecting a source name ofthe DSN retrieval request as the one or more properties of the DSNretrieval request, performing a series of functions on the source namebased on the plurality of storage pool identifiers and the plurality ofstorage pool weighting factors to produce a plurality of storage values,and performing a ranking function on the plurality of storage values toproduce the storage scoring resultant.

In a third scoring function approach, the performing the scoringfunction includes the processing module selecting a storage unit levelindication as a resource level, selecting a storage site-storage unitidentifier and a storage site-storage weighting factor for each of theplurality of storage units to produce a plurality of storagesite-storage unit identifiers and a plurality of storage site-storageunit weighting factors, where the one or more properties of DSN memoryincludes the plurality of storage site-storage unit identifiers and theplurality of storage site-storage unit weighting factors, selecting asource name of the DSN retrieval request as the one or more propertiesof the DSN retrieval request, performing a series of functions on thesource name based on the plurality of storage site-storage unitidentifiers and the plurality of storage site-storage unit weightingfactors to produce a plurality of storage values, and performing aranking function on the plurality of storage value to produce thestorage scoring resultant.

The method continues at step 434 where the processing module identifiesa set of primary storage units of the plurality of storage units basedon the storage scoring resultant (e.g., a set of storage unitsassociated with a highest score of the storage scoring resultant). Themethod continues at step 436 where the processing module sends a set ofretrieval requests to the set of primary storage units regarding the DSNretrieval request. When a primary storage unit of the set of primarystorage units does not provide a favorable response to a correspondingone of the set of retrieval requests regarding a corresponding portionof the data object, the method continues at step 438 where theprocessing module uses the storage scoring resultant to identify analternative storage unit of the plurality of storage units regarding thecorresponding portion of the data object. When the alternative storageunit is not identified, the method branches to step 442. When thealternative storage unit is identifying, the method continues to step440.

When the alternative storage unit is identified, the method continues atstep 440 where the processing module sends the corresponding one of theset of retrieval requests to the alternative storage unit. When thealternative storage unit is not identified, the method continues at step442 where the processing module issues a rebuilding function for thecorresponding portion of the data object, where a rebuilt correspondingportion of the data object is to be stored in the alternative storageunit. The method continues at step 444 where the processing moduledetermines whether the primary storage unit is available for storing therebuilt corresponding portion of the data object. When the primarystorage unit is available for storing the rebuilt corresponding portionof the data object, the method continues at step 446 where theprocessing module transfers the rebuilt corresponding portion of thedata object from the alternative storage unit to the primary storageunit.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIGS. 42A and 42C are a schematic block diagram of another embodiment ofa dispersed storage network (DSN) that includes a storage unit set 460,the network 24 of FIG. 1, and a plurality of distributed storage andtask (DST) integrity processing units 1-R. The storage unit set 460includes a set of DST execution (EX) units 1-n. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1. EachDST execution unit may be interchangeably referred to as a storage unitand the storage unit set 460 may be interchangeably referred to as a setof storage units. Each DST integrity processing unit includes adecentralized agreement module 462 and the DST client module 34 ofFIG. 1. The decentralized agreement module 462 may be implementedutilizing the decentralized agreement module 350 of FIG. 40A. Each DSTintegrity processing unit may be implemented utilizing the DST integrityprocessing unit 20 of FIG. 1. Hereafter, the DST integrity processingunit may be interchangeably referred to as a DSN unit and the pluralityof DST integrity processing units may be interchangeably referred to asa plurality of DSN units. The DSN functions to identify a task executionresource of the DSN. For example, the DSN identifies a particular DSTintegrity processing unit to rebuild an encoded data slice associatedwith a storage error (e.g., missing, corrupted), where a data object isdivided into a plurality of data segments and each data segment isdispersed storage error encoded to produce a plurality of sets ofencoded data slices that are stored in the storage unit set 460.

FIG. 42A illustrates steps of an example of operation of the identifyingof the task execution resource and in particular steps of an example ofoperation of the rebuilding of the encoded data slice where theplurality of DSN units determines to perform a DSN level task for arange of DSN addresses. The DSN level task includes one of a rebuiltscan function, a rebuilding encoded data slices function (e.g., scanningfor errors, rebuilding encoded data slices when errors are detected), astorage unit utilization analysis (e.g., storage unit capacity, astorage unit failure rate, a storage unit efficiency level, a storageunit speed of access level, a storage unit availability level, a storageunit replacement schedule, and storage unit expansion, etc.), datamigration (e.g., transferring encoded data slices from the storage unitset 460 another storage unit set), and a distributed computing partialtask. For example, the DST client module 34 of the plurality of DSTintegrity processing units (e.g., the plurality of DSN units) determinesto perform the DSN level task for a DSN address range 1 is associatedwith the set of DST execution units of the storage unit set 460, where aDSN address range is associated with the storage unit set 460 and wherethe DSN address range includes the DSN address range 1.

The determining to perform the DSN level task for the range of DSNaddresses includes the plurality of DSN units accessing a centralizedsystem registry that includes DSN level tasks, scheduling informationregarding the DSN level tasks, and ranges of DSN address regarding theDSN level tasks, and, based on the scheduling information, determiningthat the DSN level task for the range of DSN addresses is to beperformed. For example, each DST client module 34 of each DST integrityprocessing unit interprets the scheduling information regarding the DSNlevel tasks from the centralized system registry to determine thattiming of performing a DSN level task to scan for storage errorsassociated with either corrupted or missing stored encoded data slicesfor the range of DSN addresses is favorable (e.g., in accordance with aschedule of the scheduling information).

Having determined to perform the DSN level task, each of the pluralityof DSN units executes a scoring function using one or more properties ofthe range of DSN addresses (e.g., individual DSN address, some or all ofthe DSN addresses, a source name, a range of source names, an individualslice name, a range of slice names) and one or more properties of eachof the plurality of DSN units (e.g., weighting factors and identifiers)to produce a scoring resultant. The one or more properties of the rangeof DSN addresses includes one of an individual DSN address, at leastsome DSN addresses in the range of DSN addresses, a source namecorresponding to a data object, a set of source names corresponding to aset of data objects, an individual slice name, and a range of slicenames. The one or more properties of each of the plurality of DSN unitsincludes a plurality of identifiers for the plurality of DSN units(e.g., identifiers of DST integrity processing units 1-R), and aplurality of weighting factors for the plurality of DSN units, where theplurality of weighting factors are specific for the DSN level task(e.g., weighting factors for each of the DST integrity processing unitswith regards to rebuilding encoded data slices as extracted from thesystem registry).

The executing of the scoring function includes the DST client module 34accessing the centralized system registry that includes a plurality ofDSN level tasks, a plurality of DSN unit identifiers, and pluralities ofweighting factors corresponding to the plurality of DSN level tasks,where, the plurality of weighting factors of the pluralities ofweighting factors are specific for the DSN level task of the pluralityof DSN level tasks. The executing of the scoring function furtherincludes the decentralized agreement module 462 of each of the DSN unitsgenerating a score for each of the DSN units to produce a plurality ofscores and ranking the plurality of scores to produce ranked scoringinformation 466 that includes the scoring resultant. For example, theDST client module 34 issues a ranked scoring information request 464 tothe decentralized agreement module 462 and receives the ranked scoringinformation 466 from the decentralized agreement module 462. The rankedscoring information request 464 includes one or more of the one or moreproperties of the range of DSN addresses (e.g., a sub-DSN address rangeof the DSN address range of the set of DST execution units) and the oneor more properties of each of the plurality of DSN units (e.g.,weighting factors of the DST integrity processing units and identifiersof the DST integrity processing units).

Having produced the scoring resultant, each DSN unit identifies a DSNunit of the plurality of DSN units to execute the DSN level task basedon the scoring resultant. For example, each DST client module 34interprets the scoring resultant to identify a DST integrity processingunit associated with a highest score of the plurality of scores as theidentified DSN unit. For instance, each DST integrity processing unitidentifies the DST integrity processing unit 2 as the identified DSNunit to perform the DSN level task that includes the scanning of thestored encoded data slices associated with the DSN address range 1.

Having identified the DSN unit of the plurality of DSN units to executethe DSN level task, the identified DSN unit executes the DSN level taskfor the range of DSN addresses. For example, the DST integrityprocessing unit 2 executes the scanning task by issuing, via the network24, error detection requests 468 (e.g., list slice requests) to the setof DST execution units, receiving, via the network 24, from at leastsome of the DST execution units error detection responses 470 (e.g.,list slice responses), and interpreting the error detection responses470 to identify one or more slice names associated with one or morestorage errors 472. For instance, the DST client module 34 of the DSTintegrity processing unit 2 identifies a slice name of a missing encodeddata slice when the list slice responses includes slice names of otherencoded data slices a set of encoded data slices that includes themissing encoded data slice but not the slice name of the missing encodeddata slice.

When executing the DSN level task for the range of DSN addresses thatincludes scanning for storage errors, the DST client module 34 of theidentified DSN unit issues the storage error 472 to include the one ormore slice names (e.g., a list of slice names of encoded data slices fora subsequent rebuilding DSN level task) associated with the one or moredetected storage errors. The issuing may include one or more of sending,via the network 24, the storage error 472 to at least some of the otherDST integrity processing units, and storing the storage error 472 as atleast one set of encoded storage error slices within the storage unitset 460 enabling subsequent recovery of the storage error 472 by each ofthe other DST integrity processing units.

FIGS. 42B and 42D are a schematic block diagram of another embodiment ofa distributed storage and task (DST) integrity processing unit thatincludes the DST client module 34 of FIG. 1 and the decentralizedagreement module 462 of FIG. 42A. The decentralized agreement module 462includes a plurality of deterministic functions 1-R, a plurality ofnormalizing functions 1-R, a plurality of scoring functions 1-R, and aranking function 474. Each deterministic function may be implementedutilizing the deterministic function of FIG. 40A. Each normalizingfunction may be implemented utilizing the normalizing functions of FIG.40A. Each scoring function may be implemented utilizing the scoringfunction of FIG. 40A. The ranking function 474 may be implementedutilizing the ranking function 352 of FIG. 40A.

FIG. 42B illustrates further steps of the example of operation of theidentifying of the task execution resource including further steps ofthe example of operation of the rebuilding of the encoded data slicewith regards to the operation of the DST integrity processing unit 2 ofFIG. 42A. The further steps of the example of operation includes the DSTintegrity processing unit 2 executing the scoring function, where, foreach of the plurality of DSN units (e.g., DST integrity processing units1-R) the DST client module 34 obtains an error scan message (MSG) 476(e.g., a scan request, and interpretation of a scanning schedule, aninterpretation of an error message, the range of DSN addresses, etc.)and generates the ranked scoring information request 464 to include oneor more of a sub-DSN address range 478 as the one or more properties ofthe range of DSN addresses (e.g., the DSN address range 1 associatedwith the scanning DSN level task), identifiers of a plurality ofrebuilders 1-R as the plurality of DSN units (e.g., the DST integrityprocessing units 1-R), and the weighting factors for the plurality ofDSN units (e.g., rebuilders 1-R weights). Having produced the rankedscoring information request 464, the DST client module 34 sends theranked scoring information request 464 to the decentralized agreementmodule 462.

Having received the ranked scoring information request 464, adeterministic function of the plurality of deterministic functions 1-Rperforms a first function (e.g., a deterministic function) based on anidentifier of one of the plurality of DSN units and the one or moreproperties of the range of DSN addresses (e.g., the sub-DSN address 478)to produce an interim result. For example, the deterministic function 3performs the deterministic function on the sub-DSN address 478 and therebuilder 3 identifier to produce an interim result 3.

With the interim result produced, a normalizing function of theplurality of normalizing functions 1-R normalizes the interim result toproduce a normalized result. For example, the normalizing function 3performs the normalizing function on the interim result 3 to produce anormalized interim result 3. With the normalized interim resultproduced, a scoring function of the plurality of scoring functions 1-Rperforms a second function (e.g., a scoring function) based on thenormalized result and a weighting factor for the one of the plurality ofDSN units to produce a score. For example, scoring function 3 performsthe scoring function on the normalized interim result 3 using therebuilder 3 weight to produce a score 3. With the scores 1-R produced,the ranking function 474 ranks the scores for each of the plurality ofDSN units (e.g., scores 1-R) to produce the scoring resultant (e.g., theranked scoring information 466). For example, the ranked scoringinformation 466 indicates that a highest ranked score is associated withrebuilder 2 (e.g., DST integrity processing unit 2).

With the ranked scoring information 466 produced, the DST client module34 interprets the ranked scoring information 466 to identify the DSTintegrity processing unit 2 as the identified DSN unit to execute theDSN level task (e.g., the scanning for errors task). Having identifiedthe DST integrity processing unit 2 as the identify DSN unit, the DSTclient module 34 issues the error detection request 468 to the DSTexecution units, receives the error detection responses 470, and issuesthe storage error 472 to include the slice names associated with thestorage errors.

FIG. 42C illustrates further steps of the example of operation of theidentifying of the task execution resource including further steps ofthe example of operation of the rebuilding of the encoded data slicewhere the plurality of DSN units (e.g., the DST integrity processingunits 1-R) determines to perform the DSN level task the range of DSNaddresses (e.g., individual slice names of DSN address range 1). Forexample, the DST integrity processing unit 1 determines to perform therebuilding encoded data slice function as the DSN level task. Forinstance, the DST client module 34 of the DST integrity processing unit1 determines that the DST integrity processing unit 1 has availablecapacity for performing the rebuilding encoded data slices function.

When one or more DSN units are available to perform the rebuildingencoded data slices function, the determining to perform the DSN leveltask for the range of DSN addresses includes the one or more DSN unitsof the plurality of DSN units receiving a rebuild list of encoded dataslices (e.g., the storage error 472) and in response to receiving therebuild list, determines that the DSN level task is rebuilding is to beperformed. For example, DST integrity processing units 1-5 determine toperform the DSN level task for the range of DSN addresses (e.g., slicenames of the rebuild list) when receiving the rebuild list of encodeddata slices.

Having determined to perform the DSN level task, each of the one or moreDSN units of the plurality of DSN units executes the scoring functionusing the one or more properties of the range of DSN addresses (e.g.,the one or more slice names of the encoded data slices associated withthe one or more storage errors) and the one or more properties of eachof the plurality of DSN units (e.g., weighting factors of the DSTintegrity processing units and identifiers of the DST integrityprocessing units) to produce a scoring resultant for the rebuilding DSNlevel task. The executing of the scoring function includes the DSN unit(e.g., the DST client module 34 of the DST integrity processing unit 1)accessing the centralized system registry to obtain the plurality of DSNunit identifiers and the plurality of weighting factors of the pluralityof DSN units and issuing the ranked scoring information request 464 tothe decentralized agreement module 462. The decentralized agreementmodule 462 generates a score for each of the DSN units to produce aplurality of scores for each slice name of the rebuild list of encodeddata slices and ranks the plurality of scores to produce the rankedscoring information 466 as the scoring resultant.

With the scoring resultant produced, each DSN unit identifies a DSN unitof the plurality of DSN units to execute the rebuilding DSN level taskbased on the scoring resultant. For example, each DST client module 34of the plurality of DST integrity processing units identifies the DSTintegrity processing unit 1 as the identified DSN unit when the DSTintegrity processing unit 1 is associated with a highest score of theplurality of scores. Having identified the DSN unit to execute therebuilding DSN level task, the identified DSN unit executes therebuilding DSN level task for the range of DSN addresses. For example,the DST client module 34 of the DST integrity processing unit 1 issues,via the network 24, rebuilding slice requests to the set of DSTexecution units, receives rebuilding slices 480, dispersed storage errordecodes the received rebuilding slices 480 to produce one or morerebuilt encoded data slices 482, and sends, via the network 24, the oneor more rebuilt encoded data slices 482 to one or more associated DSTexecution units of the storage unit set 460 for storage

FIG. 42D illustrates further steps of the example of operation of theidentifying of the task execution resource including further steps ofthe example of operation of the rebuilding of the encoded data slicewith regards to the operation of the DST integrity processing unit 1 ofFIG. 42C. The further steps of the example of operation includes the DSTclient module 34 obtaining a repair error message (MSG) 484, where therepair error message 484 includes one or more of a rebuilding request,an interpretation of a list of slice names of encoded data slice to berebuilt, and an interpretation of an error message. For example, the DSTclient module 34 accesses a dispersed hierarchical index that includesthe list of slice names of encoded data slices to be rebuilt.

Having obtained the list of slice names, the DST client module 34 issuesthe ranked scoring information request 464 to the decentralizedagreement module 462, where the ranked scoring information request 464includes one or more of a slice name 486 of the list of slice names, arebuilding task identifier, the identifiers of the rebuilders 1-R, andthe weighting factors of the rebuilders 1-R associated with therebuilding DSN level task.

Each of the deterministic functions 1-R performs the first function onthe identifier of one of the plurality of DSN units and the one or moreproperties of the range of DSN addresses to produce an interim result.For example, the deterministic function 2 performs the deterministicfunction on the slice name 486 and the rebuilder 2 identifier to producean interim result 2 of interim results 1-R. Each normalizing functionperforms a normalizing function on the interim result to produce anormalized result. For example, the normalizing function 2 performs thenormalizing function on the interim result 2 to produce a normalizedinterim result 2 of normalized interim results 1-R. Each scoringfunction performs second function (e.g., the scoring function) based onthe normalized result and a weighting factor for the one of theplurality of DSN units to produce a score. For example, the scoringfunction 2 performs the scoring function on the normalized interimresult 2 using the rebuilder 2 weight to produce a score 2 of scores1-R.

With the scores 1-R produced, the ranking function 474 ranks the scoresfor each of the plurality of DSN units to produce the ranked scoringinformation 466 as the scoring resultant. For example, the rankingfunction 474 identifies a highest score associated with rebuilder 1(e.g., DST integrity processing unit 1). Having produced the rankedscoring information 466, the decentralized agreement module 462 sendsthe ranked scoring information 466 to the DST client module 34.

The DST client module 34 determines that the DST integrity processingunit 1 is the identified DSN unit when the score associated with the DSTintegrity processing unit 1 is the highest score of the ranked scoringinformation 466. When the DST integrity processing unit 1 is identifiedDSN unit, the DST client module 34 issues rebuilding slice requests 488to the DST execution units, receives the rebuilding slices 480,dispersed storage error decodes the rebuilding slices 480 produce theone or more encoded data slices as the rebuilt slice 482, andfacilitates storage of the rebuilt slice 482 in an associated storageunit.

FIG. 42E is a flowchart illustrating an example of identifying a taskexecution resource. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-39, 42A-D, and also FIG. 42E. The method beginsor continues at step 500 where a processing module of a computing deviceof one or more computing devices (e.g., a plurality of DSN units) of adispersed storage network (DSN) determines to perform a DSN level taskfor a range of DSN addresses (e.g., a storage error scanning function, arebuilding encoded data slices function). For example, when scanning,the processing module determines to perform the DSN level task for therange of DSN addresses by accessing a centralized system registry thatincludes DSN level tasks, scheduling information regarding the DSN leveltasks, and ranges of DSN address regarding the DSN level tasks, and,based on the scheduling information, determines that the DSN level taskfor the range of DSN addresses is to be performed. As another example,when rebuilding encoded data slices, the processing module determines toperform the DSN level task for the range of DSN addresses by receiving arebuild list of encoded data slices, and in response to receiving therebuild list, determines that the DSN level task is rebuilding is to beperformed.

The method continues at step 502 where the processing module executes ascoring function using one or more properties of the range of DSNaddresses and one or more properties of each of a plurality of DSN unitsto produce a scoring resultant. For example, the processing moduleaccesses the centralized system registry that includes a plurality ofDSN level tasks, a plurality of DSN unit identifiers, and pluralities ofweighting factors corresponding to the plurality of DSN level tasks,where, the plurality of weighting factors of the pluralities ofweighting factors are specific for the DSN level task of the pluralityof DSN level tasks; generates, by each of the DSN units, a score foreach of the DSN units to produce a plurality of scores; and ranks theplurality of scores to produce the scoring resultant. As anotherexample, the processing module performs a first function (e.g., adeterministic function) based on an identifier of one of the pluralityof DSN units and the one or more properties of the range of DSNaddresses to produce an interim result, normalizes (e.g., utilizing thenormalizing function) the interim result to produce a normalized result,performs a second function (e.g., a scoring function) based on thenormalized result and a weighting factor for the one of the plurality ofDSN units to produce a score, and ranks the scores for each of theplurality of DSN units to produce the scoring resultant.

The method continues at step 504 where the processing module identifiesa DSN unit of the plurality of DSN units to execute the DSN level taskbased on the scoring resultant. For example, the processing moduleidentifies a highest score of the scoring resultant and selects a DSNunit associated with the highest score as the identified DSN unit. Themethod continues at step 506 where the identified DSN unit executes theDSN level task for the range of DSN addresses. The method describedabove may utilize two or more iterations to perform a related to or moreDSN level tasks. For example, the plurality of DSN units determines toscan for storage errors in a first iteration and determines to rebuildencoded data slices associated with the storage errors in a seconditeration.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a DST execution (EX) unit set 510. The DST processing unit 16includes the DST client module 34 of FIG. 1. The DST execution unit set510 includes a set of DST execution units 1-n. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1. EachDST execution unit includes a plurality of memory devices 1-M.

The DSN functions to select a set of memory devices for storage of data,where a data segment of the data is dispersed storage error encoded toproduce a set of encoded data slices for storage in the selected set ofmemory devices. In an example of operation of selecting the set ofmemory devices, each DST execution unit temporarily associates eachmemory device of the plurality of memory devices of the DST executionunit to a sub-DSN address range based on one or more attributes of thememory device, where each DST execution unit is associated with a commonDSN address range (e.g., source name range).

For example, DST execution unit 1 identifies common attributes of agiven memory device of the DST execution unit 1 with a correspondingmemory device of each of the other DST execution units. For instance,each DST execution unit identifies a memory device of a first sub-DSNaddress range as a first memory device when each of the first memorydevices shares a common group of attributes (e.g., same manufacture,same model memory device, same software version, similar hardwareoptions.

Having associated the memory devices, the DST execution unit receives anaccess request 512 (e.g., write request, a read request, delete request,a list request, etc.) from the DST client module 34, where the accessrequest 512 includes a slice name. Having received the access request512, the DST execution unit identifies a sub-DSN address range of aplurality of sub-DSN address ranges, where the identified sub-DSNaddress range includes the slice name.

Having identified the sub-DSN address range, the DST execution unitgenerates an offset sub-DSN address range based on the identifiedsub-DSN address range and an upset function. The offset function mayinclude incrementing the sub-DSN address range by a pillar index numberof the slice name. For example, DST execution unit 2 utilizes a pillarindex of 2, DST execution unit 3 utilizes a pillar index of 3.

Having generated the offset sub-DSN address range, the DST executionunit identifies a memory device associated with the offset sub-DSNaddress range. For example, DST execution unit 2 identifies memorydevice 2 when the offset sub-DSN address range is a second sub-DSNaddress range. As such, each DST execution unit of the set of DSTexecution units selects memory devices with different attributes whichmay provide a system performance improvement from diversity of memorydevice attributes types (e.g., avoiding potential correlated failures).

Having identified the memory device, the DST execution unit executes theaccess request 512 using identify memory device and issues an accessresponse 514 to the DST client module 34. For example, when the accessrequest 512 includes a storage request, the DST execution unit stores areceived encoded data slice in the identified memory device and issuesan access response 514 indicating successful storage. For instance, DSTexecution unit 1 stores a first encoded data slice of the set of encodeddata slices in memory device 1 of the DST execution unit 1, DSTexecution unit 2 stores a second encoded data slice of the set ofencoded data slices in memory device 2 of the DST execution unit 2, etc.

FIG. 43B is a flowchart illustrating an example of selecting a memorydevice. The method begins or continues at step 516 where a processingmodule (e.g., of a storage unit) coordinates, with other storage unitsof a set of storage units that includes the storage unit, association ofcommon attribute memory devices by sub-DSN address range of a DSNaddress range associated with a set of storage units. The coordinationmay include one or more of interpreting a common system registry,receiving configuration information, and exchange and configurationinformation.

The method continues at step 518 where the processing module receives anencoded data slice access requests that includes a slice name. Themethod continues at step 520 where the processing module identifies asub-DSN address range that includes the slice name. The identifyingincludes at least one of interpreting a slice name to DSN address rangetable, performing a deterministic function on the slice name to producean identifier of the sub-DSN address range, initiating a query, andreceiving a query response.

The method continues at step 522 where the processing module generatesan offset sub-DSN address range based on the identified sub-DSN addressrange and in offset function of the storage unit. For example, theprocessing module identifies the access function and utilizes the offsetfunction on the identified sub-DSN address range to produce the offsetsub-DSN address range. For instance, the processing module adds a pillarindex of the storage unit to the sub-DSN address range to produce theoffset sub-DSN address range.

The method continues at step 524 of the processing module identifies amemory device associated with the offset sub-DSN address range. Forexample, the processing module interprets a sub-DSN address range tomemory device identifier table using the offset sub-DSN address range toproduce an identifier of the memory device. The method continues at step526 where the processing module facilitates the access request using theidentified memory device. For example, the processing module causesexecution of the access request and generation of an access response.

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a DST execution (EX) unit set 530. The DST processing unit 16includes the DST client module 34 of FIG. 1. The DST execution unit set530 includes a set of DST execution units 1-n. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1. EachDST execution unit includes a plurality of memory devices 1-M.

The DSN functions to rebuild an encoded data slice associated with anunavailable (e.g., failed, off-line) memory device, where a data segmentof data is dispersed storage error encoded to produce a set of encodeddata slices that includes the encoded data slice and where the set ofencoded data slices is stored in a set of memory devices of the set ofDST execution units. Each set of memory devices includes memory devicesassociated with one or more common attributes. For example, each memorydevice 1 of each DST execution unit is associated with a commonmanufacturer and common model number, each memory device 2 of each DSTexecution unit is associated with another common manufacturer andanother common model number etc.

In an example of operation of the rebuilding of the encoded data slice,the DST client module 34, or any other module or unit capable ofrebuilding encoded data slices, detects a storage error associated withan unavailable memory device of a DST execution unit. The detectingincludes at least one of receiving a failed memory message, interpretinga memory test result, and detecting a missing slice. For example, theDST client module 34 receives a message indicating that memory device 2of DST execution unit 2 has failed.

Having detected the storage error, the DST client module 34 determinesattributes of the unavailable memory device. The attributes includes atleast one of a manufacture, a model number, a serial number, a softwareversion, a hardware configuration version, an estimated end-of-lifetimeframe, and available capacity level, a performance level, and an agetimeframe. The determining includes at least one of initiating a query,interpreting a query response, interpreting system registry information,and receiving an attributes list.

Having determined the attributes of the unavailable memory device, theDST client module 34 determines attributes of the other memory devices.For example, the DST client module 34 determines attributes of remainingmemory devices of the DST execution unit 2. As another example, the DSTclient module 34 determines attributes of memory devices of other DSTexecution units. For instance, the DST client module 34 determinesattributes of memory devices 1-M of DST execution unit 1.

Having determined the attributes of the other memory devices, the DSTclient module 34 selects another memory device based on the attributesof the other memory device and the attributes of the unavailable memorydevice. For example, the DST client module 34 selects the other memorydevice when the other memory devices associated with attributes thatcompare favorably (e.g., substantially the same) with the attributes ofthe available memory device. For instance, the DST client module selectsa memory device 2 of the DST execution unit 1 when the attributes of thememory device to the DST execution unit 1 compare favorably to thememory device 2 of the DST execution unit 2.

Having selected the other memory device, the DST client module 34rebuilds at least one encoded data slice associated with the storageerror to produce one or more rebuilt encoded data slices. For example,the DST client module 34 issues, via the network 24, rebuild requests532 to recover encoded data slices of a set of encoded data slices B-1through B-n, where encoded data slice B-2 requires rebuilding, receives,via the network 24, rebuilding responses 534 that includes a decodethreshold number of encoded data slices of the set of encoded dataslices, dispersed storage error decodes the decode threshold number ofencoded data slices to produce the recovered data segment, and dispersedstorage error encodes the recovered data segment to produce a rebuiltencoded data slice B-2.

Having produced the one or more rebuilt encoded data slices, the DSTclient module 34 facilitates storage of the one or more rebuilt encodeddata slices in the selected other memory device. For example, the DSTclient module 34 issues, via the network 24, another rebuilding request532 to the DST execution unit 1 that includes the rebuilt encoded dataslice B-2 for storage in the memory device 2 of the DST execution unit1. Having facilitated the storage of the one or more rebuilt encodeddata slices, the DST client module 34 associates slice names of the oneor more rebuilt encoded data slices with the selected other memorydevice and disassociates the slice names from the unavailable memorydevice. For example, the DST client module 34 updates a dispersedhierarchical index to indicate that rebuilt encoded data slice B-2 isavailable at memory device 2 of DST execution unit 1 and is notavailable from DST execution unit 2.

FIG. 44B is a flowchart illustrating another example of selecting amemory device. The method begins or continues at step 536 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) detects a storage error associated with an unavailable memorydevice of a storage unit of a set of storage units, where a data segmentof data is dispersed storage error encoded to produce a set of encodeddata slices that is stored in a set of memory devices of the set ofstorage units. The set of memory devices includes the unavailable memorydevice.

The method continues at step 538 where the processing module identifiesattributes of the unavailable memory device. The identifying includes atleast one of interpreting a system registry, interpreting a memorydevice list, initiating a query, and interpreting a received queryresponse. The method continues at step 540 where the processing moduleidentifies attributes of other memory devices. The identifying includesidentifying other memory devices associated with the set of storageunits and identifying attributes of the identified other memory devices.

The method continues at step 542 where the processing module selects oneof the other memory devices based on the attributes of the other memorydevices and the attributes of the unavailable memory device. Forexample, the processing module identifies a memory device associatedwith attributes that compare favorably to the attributes of theunavailable memory device. For instance, the processing module selectsthe other memory device that includes a common software version with theunavailable memory device.

The method continues at step 544 where the processing module rebuilds atleast one encoded data slice associated with the storage error toproduce one or more rebuilt encoded data slices. As a specific example,for each encoded data slice to be rebuilt, the processing module obtainsa decode threshold number of encoded data slices of the set of encodeddata slices that includes the at least one encoded data slice associatedwith the storage error, dispersed storage error decodes the decodethreshold number of obtained encoded data slices to produce a recovereddata segment, and dispersed storage error encodes the recovered datasegment to produce a rebuilt encoded data slice.

The method continues at step 546 where the processing module facilitatesstorage of the one or more rebuilt encoded data slices in the selectedone of the other memory devices. For example, the processing moduleissues a write slice request to another storage unit associated with theselected one of the other memory devices, where the write slice requestincludes the rebuilt encoded data slice when utilizing the memory deviceof the other storage unit. As another example, the processing modulestores the rebuilt encoded data slice in the selected other memorydevice when the selected other memory devices associated with thestorage unit of the unavailable memory device.

The method continues at step 548 where the processing module associatesslice names of the one or more rebuilt encoded data slices with theselected one of the other memory devices. As a specific example, theprocessing module updates a dispersed hierarchical index to associatethe slice names with the selected memory device and disassociates theslice names from the unavailable memory device.

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a DST execution (EX) unit pool 550. The DST processing unit 16includes the DST client module 34 of FIG. 1. The DST execution unit pool550 includes a plurality of DST execution units. For example, the DSTexecution unit pool hundred and 50 includes a set of DST execution units1, 2A, 2B, 3, 4, etc., through DST EX unit n. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1. EachDST execution unit includes a plurality of memory devices 1-M.

The DSN functions to migrate encoded data slices associated with one ormore memory devices of a DST execution unit being de-commissioned, wherea data segment of data is dispersed storage error encoded to produce aset of encoded data slices and where the set of encoded data slices isstored in a set of memory devices of the plurality of DST executionunits. In an example of operation of the migrating, the DST clientmodule 34 identifies a DSN address range associated with encoded dataslices stored in one or more memory devices to be de-commissioned. Theidentifying includes at least one of interpreting a test result,receiving a manager input, interpreting an end of life schedule, anddetecting storage errors. For example, the DST client module 34interprets the end of life schedule indicating de-commissioning of DSTexecution unit 2A (e.g., and each memory device of the plurality ofmemory devices 1-M) and performs a DSN address range look upcorresponding to the DST execution unit 2A to produce an identified DSNaddress range.

Having identified the DSN address range, the DST client module 34identifies one or more replacement memory devices. The identifyingincludes at least one of receiving a commissioning message, interpretinga new configuration, interpreting a system registry, receiving a managerinput, and interpreting a replacement schedule. For example, the DSTclient module 34 detects DST execution unit 2B is available as beingcommissioned to replace DST execution unit 2A and identifies at leastsome of the memory devices 1-M of the DST execution unit 2B.

When a favorable number of other memory devices are available of a setof memory devices that includes the one or more memory devices to bedecommissioned, the DST client module 34 facilitates disabling the DSNaddress range. For example, the DST client module 34 determines that awrite threshold number of memory devices are available amongst theplurality of DST execution units, where the write threshold number ofmemory devices does not include the one or more memory devices to bedecommissioned. The disabling includes at least one of establishing astatus indicator indicating that the DSN address ranges disabled,issuing a write slice request to each of the set of memory devices(e.g., without issuing a commit request to produce a lock on slice namesassociated with the DSN address range), and issuing a deactivationrequest to the DST execution unit being de-commissioned.

Having disabled the DSN address range, the DST client module 34facilitates migration of the encoded data slices from the one or morememory devices to be de-commissioned to the replacement memory devices.For example, the DST client module 34 issues, via the network 24,migration requests 552 to the DST execution unit 2A to facilitatetransfer of the encoded data slices from the memory devices 1-M to theDST execution unit 2B for storage. The DST execution unit 2B issues, viathe network 24, migration responses to the DST client module 34indicating a status of the migration. As another example, the DST clientmodule 34 issues other migration requests 552 to the DST execution unit2A to recover the encoded data slices, receives migration responses 554that includes the encoded data slices, and issues further migrationrequests to the DST execution unit 2B that include the encoded dataslices for storage.

When the encoded data slices have been successfully migrated, the DSTclient module 34 enables the DSN address range. The enabling includes atleast one of disabling the one or more memory devices, updating at leastone of the system registry, a DSN directory, and a dispersedhierarchical index to associate the DSN address range with thereplacement memory devices and to disassociate the DSN address rangewith the one or more memory devices.

FIG. 45B is a flowchart illustrating an example of migrating encodeddata slices. The method begins or continues at step 556 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) identifies a dispersed storage network (DSN) address rangeassociated with encoded data slices stored in one or more memory devicesto be decommissioned. The identifying includes one or more ofinterpreting a test, receiving a managing unit input, interpreting anend-of-life schedule, detecting storage errors greater than a storageerror level, and performing a slice name in DSN address range to memorydevice table lookup.

The method continues at step 558 where the processing module identifiesone or more replacement memory devices. The identifying includes atleast one of receiving a commissioning message, interpreting a new DSNconfiguration, interpreting a system registry, receiving a managerinput, and interpreting a replacement schedule. When a favorable numberof other memory devices are available of a set of memory devices thatincludes the one or more memory devices to be decommissioned, the methodcontinues at step 560 where the processing module disables the DSNaddress range. The disabling includes one or more of determining that atleast a threshold number of available other memory devices areavailable, issuing a status indicator, issuing a write slice requestswithout sending a commit transaction request for slice names associatedwith the DSN address range, and issuing a deactivation request.

The method continues at step 562 where the processing module facilitatesmigration of the encoded data slices from the one or more memory devicesto be decommissioned to the replacement memory devices. For example, theprocessing module recovers encoded data slices from the one or morememory devices and stores the recovered encoded data slices in thereplacement memory devices. As another example, the processing moduleissues a migration request to at least one of a storage unit associatedwith the one or more memory devices to be decommissioned and at leastone storage unit associated with the replacement memory devices.

When the encoded data slices have been successfully migrated, the methodcontinues at step 564 where the processing module enables the DSNaddress range. The enabling includes one or more of detecting that theencoded data slices have been successfully migrated, issuing an updatedstatus indicator, updating one or more of a system registry, a DSNdirectory, and a DSN index to indicate association of the DSN addressrange with the replacement memory devices and to disassociate the DSNaddress range from the one or more memory devices to be decommissioned.

FIG. 46A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and at least two DST execution (EX) unit sets 1 and 2. The DSTprocessing unit 16 includes the DST client module 34 of FIG. 1. Each DSTexecution unit set includes a set of DST execution units 1-n utilized tostore sets of encoded data slices, where a data segment of data isdispersed storage error encoded to produce a set of encoded data slicesfor storage in the DST execution unit set.

The DSN functions to temporarily store rebuilt encoded data slices. Inan example of operation of the temporary storage of the rebuilt encodeddata slices, the DST client module 34 detects a failed storage unit. Thedetecting includes at least one of receiving an error message,interpreting a test result, and receiving a request. For example, theDST client module 34 detects failure of DST execution unit 2 of the DSTexecution unit set 1 when receiving an error message indicating storageunit failure. Having detected the failed storage unit, the DST clientmodule 34 identifies a DSN address range associated with the failedstorage unit (e.g., a lookup).

Having identified the DSN address range, the DST client module 34selects a plurality of storage units for storage of rebuilt encoded dataslices 576. The selecting includes at least one of initiating a query,receiving a query response, performing a lookup, and interpretingstorage unit availability information. For example, the DST clientmodule 34 selects the DST execution units 1-n of the DST execution unitset 2 for the storage of the rebuilt encoded data slices 576 when astorage unit availability information for the DST execution unit set 2is favorable compared to an availability threshold level.

For each storage unit of the selected plurality of storage units, theDST client module 34 allocates a portion of the DSN address range inaccordance with an allocation approach. The allocation approach includesone of equal allocation, allocation in proportion to available storagecapacity, allocation in proportion to available processing capacity. Forexample, the DST client module 34 allocates portions of the DSN addressrange that includes slice names of two slices to each DST execution unitof the DST execution unit set 2. For instance, the DST client module 34allocates slices A-2-1 and A-2-2 to DST execution unit 1 of the DSTexecution unit set 2, allocates slices A-2-3 and A-2-4 to DST executionunit 2 of the DST execution unit set 2, etc.

Having allocated the portion of the DSN address range, for each storageunit of the selected plurality of storage units, the DST client module34 rebuilds encoded data slices associated with the portion of the DSNaddress range to produce rebuilt encoded data slices. For example, theDST client module 34 retrieves a decode threshold number of encoded dataslices 572 from other storage units associated with the failed storageunit, dispersed storage error decodes the decode threshold number ofencoded data slices to produce a recovered data segment, and dispersedstorage error encodes the recovered data segment to produce the rebuiltencoded data slice 576. For instance, the DST client module 34 issues,via the network 24, rebuild requests 570 to the DST execution unit set 1to recover slices 572, receives rebuilding responses 574 that includesslices A-1-1, A-3-1, A-4-1, etc., decodes the decode threshold number ofencoded data slices to produce a first recovered data segment, anddispersed storage error encodes the first recovered data segment toproduce rebuilt encoded data slice A-2-1.

Having produced the rebuilt encoded data slices 576, the DST clientmodule 34, for each storage unit of the plurality of storage units,facilitates temporary storage of the rebuilt encoded data slices 576.For example, the DST client module 34 sends, via the network 24,rebuilding requests, that includes the rebuilt encoded data slices 576,to the DST execution units of the DST execution unit set 2. Forinstance, the DST client module 34 sends rebuilt encoded data sliceA-2-1 to the DST execution unit 1 of the DST execution unit set 2 inaccordance with the allocated portion of the DSN address range.

Having facilitated the temporary storage of the rebuilt encoded dataslices 576, for each storage unit of the plurality of storage units, theDST client module 34 facilitates access to the stored rebuilt encodeddata slices. For example, the processing module associates the portionof the DSN address range with the storage unit and facilitatesprocessing of access requests.

FIG. 46B is a flowchart illustrating an example of temporarily storingrebuilt encoded data slices. The method begins or continues at step 578where a processing module (e.g., of a distributed storage and task (DST)client module) detects a failed storage unit of a set of storage unitsthat stores the set of encoded data slices. The detecting includes atleast one of interpreting an error message, interpreting a test result,and receiving a storage unit status indicator. The method continues atstep 580 where the processing module identifies a dispersed storagenetwork (DSN) address range associated with the failed storage unit. Forexample, the processing module interprets a DSN address to physicallocation table.

The method continues at step 582 where the processing module selects aplurality of storage units for storage of rebuilt encoded data slices.The selecting includes at least one of initiating a query, receiving aquery response, performing a lookup, and interpreting storage unitavailability information. The selecting may include selecting one ormore storage units of the set of storage units.

For each storage unit of the selected plurality of storage units, themethod continues at step 584 where the processing module allocates aportion of the DSN address range to the storage unit. The allocation maybe in accordance with an allocation approach. The allocation approachesincludes one of evenly dividing the DSN address range by a number ofstorage units of the selected plurality of storage units, allocating inaccordance with storage unit capacity, and allocating in accordance withstorage unit processing capacity.

For each storage unit of the selected plurality of storage units, themethod continues at step 586 where the processing module rebuildsencoded data slices associated with the portion of the DSN address rangeto produce rebuilt encoded data slices. For example, the processingmodule acquires a decode threshold number of encoded data slices of theset of encoded data slices, dispersed storage error decodes the decodethreshold number of encoded data slices to produce the recovered datasegment, and dispersed storage error encodes the recovered data segmentto produce the rebuilt encoded data slice.

For each storage unit of the plurality of storage units, the methodcontinues at step 588 where the processing module facilitates temporarystorage of the rebuilt encoded data slices. For example, the processingmodule issues a write slice request that includes the encoded data sliceto the storage unit of the selected plurality of storage units. For eachstorage unit of the selected plurality of storage units, the methodcontinues at step 590 where the processing module facilitates access tothe stored rebuilt encoded data slices. The facilitating includes one ormore of associating the portion of the DSN address range with thestorage unit, executing a received access request to produce an accessresponse, and sending the access response to a requesting entity.

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a DST execution (EX) unit set 600. The DST processing unit 16includes the DST client module 34 of FIG. 1. The DST execution unit set600 includes a set of DST execution units 1-n. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1.

The DSN functions to test a DST execution unit. In an example ofoperation of the testing of the DST execution unit, the DST clientmodule 34 identifies the DST execution unit for isolation testing, wherethe DST execution unit set 600 includes the DST execution unit forisolation testing. The identifying includes at least one of interpretingan error message, interpreting a previous test result, interpreting aperformance level, receiving a request, and interpreting a testschedule. For example, the DST client module 34 identifies DST executionunit 1 for the isolation testing when the task until indicates that DSTexecution unit 1 requires the isolation testing. As another example, theDST client module 34 identifies DST execution units 1 and 2 for theisolation testing when receiving a request to test DST execution units 1and 2.

Having identified the storage unit for the isolation testing, the DSTclient module 34 determines whether a sufficient number of favorablyperforming other DST execution units of the DST execution unit set areavailable. For example, the DST client module 34 identifies thethreshold number (e.g., based on a lookup), identifies favorablyperforming DST execution units, and compares the number of favorablyperforming other DST execution units to the threshold number. Forinstance, the DST client module 34 indicates that the sufficient numberis available when 13 other DST execution units are available and thethreshold number is 13.

When the sufficient number of favorably performing other storage unitsare available, the DST client module 34 initiates the isolation testingof the identified storage unit. The initializing of the isolationtesting includes at least one of updating a status for the identifiedDST execution unit to indicate an unavailable status, inhibiting issuingaccess requests to the identified DST execution unit, and issuing one ormore isolation testing tasks to the identified DST execution unit. Forexample, the DST client module 34 issues, via the network 24, testrequests 602 to the DST execution unit 1, where the test requests 602includes one or more isolation testing tasks, and receives testresponses 604 from the DST execution unit 1. The test responses 604includes one or more results from performing the one or more isolationtesting tasks.

When the isolation testing has been completed, the DST client module 34updates the status for the identified DST execution unit to indicateavailable. The DST client module 34 generates an isolation testingreport based on the received test responses. The isolation testingreport may include one or more of a memory utilization level, a memoryfragmentation level, a number of vaults supported, a number of namespaceranges supported, a number of encoded data slices stored, a softwarerevision number, data storage statistics, data retrieval statistics, andtest failure rates.

FIG. 47B is a flowchart illustrating an example of testing a storageunit. The method begins or continues at step 606 where a processingmodule (e.g., of a distributed storage and task (DST) client module)identifies a storage unit for isolation testing, where a set of storageunits includes a storage unit. The identifying includes at least one ofreceiving a request, interpreting a schedule, interpreting an errormessage, interpreting a previous test result, and interpreting amonitored performance level.

The method continues at step 608 where the processing module determineswhether a sufficient number of favorably performing other storage unitsof the set of storage units are available during the isolation testing.For example, the processing module obtains a threshold number,identifies the favorably performing storage units, compares the numberof favorably performing other storage units to the threshold number, andindicates the sufficient number are available when the number offavorably performing other storage units compares favorably to thethreshold number (e.g., greater than or equal to).

When the sufficient number of favorably performing other storage unitsare available, the method continues at step 610 where the processingmodule initiates the isolation testing of the identified storage unit.The initializing includes at least one of updating a status for thestorage unit two indicate unavailable, issuing one or more test tasks tothe storage unit, and receiving test results. When the isolation testinghas been completed, the method continues at step 612 where theprocessing module updates the status for the storage unit to indicateavailable. The method continues at step 614 where the processing modulegenerates an isolation testing report. For example, the processingmodule interprets the received test results to produce the testingreport.

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a meta-vault 620. The DST processing unit 16 includes the DST clientmodule 34 of FIG. 1. The meta-vault 620 includes one or more sub-vaults1-3. Each sub-vault is associated with a set of DST execution units 1-n.Each DST execution unit may be implemented utilizing the DST executionunit 36 of FIG. 1.

The DSN functions to access data stored in the meta-vault 620. In anexample of operation of the accessing of the data, the DST client module34 obtains a data object for storage in the meta-vault 620. The DSTclient module 34 selects one sub-vault of the meta-vault for storage ofthe data object in accordance with a selection scheme. The selectionscheme includes at least one of a round robin approach, a DST executionunit availability-based selection, a DST execution unit performancelevel-based selection, and a DST execution unit available storagelevel-based selection.

Having selected the sub-vault, the DST client module 34 facilitatesstorage of the data object in the selected sub-vault. For example, theDST client module 34 encodes the data object to produce a plurality ofsets of encoded data slices, issues, via the network 24, one or moresets of write slice requests as access requests 622 to the set of DSTexecution units of the selected sub-vault, where the one or more sets ofwrite slice requests includes the plurality of sets of encoded dataslices, and receives access responses 624 that includes write sliceresponses indicating status of storage of the data object.

Having stored the data object, the DST client module 34 associates thedata object with the selected sub-vault. The associating includes theDST client module 34 updating at least one of a DSN directory and adispersed hierarchical index to associate a name of the data object withan identifier of the selected sub-vault.

When retrieving the data object, the DST client module 34 receives arequest to recover the data object, where the request includes the nameof the data object. Having received the request, the DST client module34 identifies a vault based on the received name of the data object. Theidentifying includes accessing at least one of the DSN directory and thedispersed hierarchical index using the name of the data object torecover the identifier of the vault.

When the identified vault is a meta-vault (e.g., is indicated by andindicator such as a system registry, the DSN directory, and thedispersed hierarchical index), the DST client module 34 identifies asub-vault for the requested data object. For example, the DST clientmodule 34 accesses a dispersed hierarchical index associated with themeta-vault to recover an identifier of the sub-vault associated withstorage of the data object.

Having identified the sub-vault, the DST client module 34 facilitatesrecovery of the data object from the identified sub-vault. For example,the DST client module 34 issues access requests 622, via the network 24,to the DST execution units of the identified sub-vault, where the accessrequests 622 includes read slice requests, receives access responses 624that includes read slice responses, and dispersed storage error decodesa decode threshold number of encoded data slices for each set of encodeddata slices of the plurality of sets of encoded data slices to reproducethe data object.

FIG. 48B is a flowchart illustrating an example of utilizing a vaultstructure in a dispersed storage network (DSN). The method begins orcontinues at step 626 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives a data object for storagein a meta-vault that includes at least two sub-vaults. The receivingincludes receiving the data object and a data name of the data object.The receiving may further include determining that the data object isassociated with the meta-vault based on a lookup.

The method continues at step 628 where the processing module selects oneof the at least two sub-vaults for storage of the data object. Theprocessing module selects the sub-vault based on a selection scheme. Themethod continues at step 630 where the processing module facilitatesstorage of the data object in the selected sub-vault. For example, theprocessing module dispersed storage error encodes the data object toproduce encoded data slices and sends the encoded data slices to a setof storage units associated with the selected sub-vault.

The method continues at step 632 where the processing module associatesthe data object with the selected sub-vault. For example, the processingmodule updates at least one of a dispersed storage network directory anda dispersed hierarchical index to associate the data name with anidentifier of the selected sub-vault.

When retrieving the data object, the method continues at step 634 wherethe processing module receives a request to recover the data object. Thereceiving includes receiving the data name of the data object. Themethod continues at step 636 where the processing module identifies avault based on the received data name. For example, the processingmodule performs a lookup using the data name to produce an identifier ofthe vault.

When the identified vault is a meta-vault, the method continues at step638 where the processing module identifies a sub-vault for the requesteddata object. The identifying includes determining that the vault is themeta-vault based on one or more of a system registry lookup, a tablelookup, initiating a query, and receiving a query response; andperforming a lookup using the data name and the identifier of themeta-vault to identify the sub-vault.

The method continues at step 640 of the processing module facilitatesrecovery of the data object from the identified sub-vault. For example,the processing module retrieves encoded data slices from the set ofstorage units associated with the identified sub-vault and dispersedstorage error decodes the retrieved encoded data slices to reproduce thedata object.

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a plurality of DST execution (EX) unit pools 1-P. The DST processingunit 16 includes a decentralized agreement module 642 and the DST clientmodule 34 of FIG. 1. The decentralized agreement module 642 may beimplemented utilizing the decentralized agreement module 350 of FIG.40A. Each DST execution unit pool includes a set of DST execution units1-n. Each DST execution unit may be implemented utilizing the DSTexecution unit 36 of FIG. 1. Each DST execution unit includes aplurality of memory devices 1-M.

The DSN functions to receive data access requests, select resources ofat least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response based on the data access. The selecting of the resourcesincludes utilizing a decentralized agreement function of thedecentralized agreement module 642, where a plurality of storageresource locations are ranked against each other. The selecting mayinclude selecting one storage pool of the plurality of storage pools,selecting DST execution units at various sites of a plurality of sites,selecting a memory of the plurality of memories for each DST executionunit, and selecting combinations of memories, DST execution units,sites, pillars, and storage pools.

In an example of operation, the DST client module 34 determines toperform a data access request. For example, the DST client module 34receives a data access request includes at least one of a store datarequest, a retrieve data request, a delete data request, a data name,and a requesting entity identifier (ID). Having determined to performthe data access request, the DST client module 34 determines a DSNaddress associated with the data access request. The DSN addressincludes at least one of a source name (e.g., including a vault ID andan object number associated with the data name), a data segment ID, aset of slice names, a plurality of sets of slice names. The determiningincludes at least one of generating (e.g., for the store data request)and retrieving (e.g., from a DSN directory) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 identifies astorage type associated with the data access request. The storage typeincludes at least one of storing a large object, storing a small object,storing a frequently accessed object, storing and infrequently accessedobject, storing a high prioritized object, and storing a low prioritizedobject. The identifying includes at least one of interpreting anindicator of the request, comparing a size of the received data forstorage to a size threshold, and accessing a historical record for thedata object that indicates frequency of access. For example, the DSTclient module 34 identifies the frequently accessed object storage typebased on interpreting the historical record of frequency of access.

Having determined the storage type, the DST client module 34 determinesranked scoring information for one or more resource levels of theplurality of DST execution unit pools based on the storage type. Thedetermining includes identifying the one or more resource levels basedon a DSN configuration, and for each level, issuing a ranked scoringinformation request 644 to the decentralized agreement module 642, wherethe request includes one or more of identifiers of storage resourcesassociated with the level, weights of the storage resources for thestorage type, and the DSN address as the asset identifier, and receivingthe ranked scoring information 646. Each resource (e.g., memory device,storage unit, storage pool) has a weight value associated with eachstorage type. For example, memory device 1 of DST execution unit 1 ofthe DST execution unit pool 1 has a weight A associated with a smalldata object storage type, and a weight B associated with the frequentlyaccessed data storage type, etc., through a weight Z for another datastorage type. In an example of the determining the ranked scoringinformation 646, the DST client module 34 obtains ranked scoringinformation 646 for the plurality of storage pools for the frequentlyaccessed object storage type as a first resource level, obtains rankedscoring information 646 for the DST execution units of a highest rankedstorage pool as a second resource level, and obtains ranked scoringinformation 646 for memory devices of each DST execution unit associatedwith highest rankings as a third resource level.

For each of the one or more resource levels, the DST client module 34selects a storage resource based on the ranked scoring information 646.For example, the DST client module 34 selects a storage pool associatedwith a highest score from the ranked scoring information of storagepools, selects a threshold number of DST execution units of the selectedstorage pool based on highest scores of the DST execution units from theranked scoring information of the DST execution units of the selectedstorage pool, and for each selected DST execution unit, selects a memorydevice of the plurality of memory devices based on the highest scores ofthe ranked scoring information of the memory devices for the selectedDST execution units.

Having selected the storage resources, the DST client module 34 accessesthe selected DST execution unit pool utilizing the selected storageresources for each of the one or more resource levels. For example, theDST client module 34 issues, via the network 24, access requests 648 tothe selected resources and receives access responses 650.

FIG. 49B is a flowchart illustrating an example of selecting storageresources. The method begins or continues at step 652 where a processingmodule (e.g., of a distributed storage and task (DST) client module)determines to perform data access in a dispersed storage network (DSN)memory. The determining includes at least one of receiving a request,determining to retrieve data, and determining to store data.

The method continues at step 654 where the processing module determinesa DSN address associated with the data access. For example, theprocessing module performs a DSN directory lookup using a data name ofthe data of the data access. The method continues at step 656 where theprocessing module identifies a storage type associated with the dataaccess. The identifying includes at least one of interpreting anindicator of the request, comparing a size of the received data forstorage to a size threshold, and accessing a historical record for thedata indicating frequency of access.

The method continues at step 658 where the processing module determinesranked scoring information for one or more resource levels of the DSNmemory. The determining includes identifying one or more resource levelsbased on configuration information of the DSN memory, performing adecentralized agreement function on one or more of the DSN address as anasset identifier, identifiers of storage resources of the one or moreresource levels, and weights of each storage resource based on thestorage type.

For each of the one or more resource levels, the method continues atstep 660 where the processing module selects storage resources based onthe ranked scoring information. For example, the processing moduleidentifies storage resources associated with a highest score versus peerresources of a common resource level. For example, by a storage pool, bya storage unit, and by memory devices.

The method continues at step 662 where the processing module accessesthe DSN memory utilizing the selected storage resource for each of theone or more resource levels. For example, the processing module accessesselected memory devices of selected storage units of a selected storagepool.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.,described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc., that mayuse the same or different reference numbers and, as such, the functions,steps, modules, etc., may be the same or similar functions, steps,modules, etc., or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A computing device of a dispersed storage network (DSN) comprises: memory; an interface; and a processing module operably coupled to the memory and the interface, wherein the processing module is operable to: determine to perform a DSN level task for a range of DSN addresses; execute a scoring function using one or more properties of the range of DSN addresses and one or more properties of each of a plurality of DSN units to produce a scoring resultant; identify a DSN unit of the plurality of DSN units to execute the DSN level task based on the scoring resultant; and instruct the identified DSN unit to execute the DSN level task for the range of DSN addresses.
 2. The computing device of claim 1, wherein the processing module determines to perform the DSN level task for the range of DSN addresses by: accessing a centralized system registry that includes DSN level tasks, scheduling information regarding the DSN level tasks, and ranges of DSN address regarding the DSN level tasks; and based on the scheduling information, determining that the DSN level task for the range of DSN addresses is to be performed.
 3. The computing device of claim 1, wherein the processing module determines to perform the DSN level task for the range of DSN addresses by: receiving, via the interface, a rebuild list of encoded data slices; and in response to receiving the rebuild list, determining, that the DSN level task is rebuilding is to be performed.
 4. The computing device of claim 1, wherein the processing module executes the scoring function by: generating a score for each of the DSN units to produce a plurality of scores; and ranking the plurality of scores to produce the scoring resultant.
 5. The computing device of claim 1, wherein the processing module determines the one or more properties of the range of DSN addresses by determining one of: an individual DSN address; at least some DSN addresses in the range of DSN addresses; a source name corresponding to a data object; a set of source names corresponding to a set of data objects; an individual slice name; and a range of slice names.
 6. The computing device of claim 1, wherein the processing module determines the one or more properties of each of the plurality of DSN units by determining: a plurality of identifiers for the plurality of DSN units; and a plurality of weighting factors for the plurality of DSN units, wherein the plurality of weighting factors are specific for the DSN level task.
 7. The computing device of claim 6, wherein the processing module is further operable to: access a centralized system registry that includes a plurality of DSN level tasks, a plurality of DSN unit identifiers, and pluralities of weighting factors corresponding to the plurality of DSN level tasks, wherein, the plurality of weighting factors of the pluralities of weighting factors are specific for the DSN level task of the plurality of DSN level tasks.
 8. The computing device of claim 1, wherein the processing module is operable to perform the DSN level task by performing one of: a rebuild scan function; a rebuilding encoded data slices function; a storage unit utilization analysis; data migration; and a distributed computing partial task.
 9. The computing device of claim 1, wherein the processing module executes the scoring function by: for each of the plurality of DSN units: performing a first function based on an identifier of one of the plurality of DSN units and the one or more properties of the range of DSN addresses to produce an interim result; normalizing the interim result to produce a normalized result; and performing a second function based on the normalized result and a weighting factor for the one of the plurality of DSN units to produce a score; and ranking the scores for each of the plurality of DSN units to produce the scoring resultant. 